scholarly journals Machine learning prediction of novel pectinolytic enzymes in Aspergillus niger through integrating heterogeneous (post-) genomics data

2021 ◽  
Vol 7 (12) ◽  
Author(s):  
Mao Peng ◽  
Ronald P. de Vries
Keyword(s):  
Machine Learning ◽  
Aspergillus Niger ◽  
Plant Cell Wall ◽  
Enzyme Characterization ◽  
Cell Wall Polysaccharide ◽  
Diverse Range ◽  
Testing Dataset ◽  
Pectinolytic Enzymes ◽  
Paper And Pulp ◽  
Bacteria And Fungi

Pectinolytic enzymes are a variety of enzymes involved in breaking down pectin, a complex and abundant plant cell-wall polysaccharide. In nature, pectinolytic enzymes play an essential role in allowing bacteria and fungi to depolymerize and utilize pectin. In addition, pectinases have been widely applied in various industries, such as the food, wine, textile, paper and pulp industries. Due to their important biological function and increasing industrial potential, discovery of novel pectinolytic enzymes has received global interest. However, traditional enzyme characterization relies heavily on biochemical experiments, which are time consuming, laborious and expensive. To accelerate identification of novel pectinolytic enzymes, an automatic approach is needed. We developed a machine learning (ML) approach for predicting pectinases in the industrial workhorse fungus, Aspergillus niger. The prediction integrated a diverse range of features, including evolutionary profile, gene expression, transcriptional regulation and biochemical characteristics. Results on both the training and the independent testing dataset showed that our method achieved over 90 % accuracy, and recalled over 60 % of pectinolytic genes. Application of the ML model on the A. niger genome led to the identification of 83 pectinases, covering both previously described pectinases and novel pectinases that do not belong to any known pectinolytic enzyme family. Our study demonstrated the tremendous potential of ML in discovery of new industrial enzymes through integrating heterogeneous (post-) genomimcs data.

scholarly journals EglC, a New Endoglucanase from Aspergillus niger with Major Activity towards Xyloglucan

2002 ◽  
Vol 68 (4) ◽  
pp. 1556-1560 ◽  
Author(s):  
Alinda A. Hasper ◽  
Ester Dekkers ◽  
Marc van Mil ◽  
Peter J. I. van de Vondervoort ◽  
Leo H. de Graaff
Keyword(s):  
Cell Wall ◽  
Aspergillus Niger ◽  
Plant Cell ◽  
Plant Cell Wall ◽  
Binding Domain ◽  
Wall Structure ◽  
Glycoside Hydrolase Family ◽  
Cell Wall Polysaccharide ◽  
Cellulose Binding Domain ◽  
Cellulose Binding

ABSTRACT A novel gene, eglC, encoding an endoglucanase, was cloned from Aspergillus niger. Transcription of eglC is regulated by XlnR, a transcriptional activator that controls the degradation of polysaccharides in plant cell walls. EglC is an 858-amino-acid protein and contains a conserved C-terminal cellulose-binding domain. EglC can be classified in glycoside hydrolase family 74. No homology to any of the endoglucanases from Trichoderma reesei was found. In the plant cell wall xyloglucan is closely linked to cellulose fibrils. We hypothesize that the EglC cellulose-binding domain anchors the enzyme to the cellulose chains while it is cleaving the xyloglucan backbone. By this action it may contribute to the degradation of the plant cell wall structure together with other enzymes, including hemicellulases and cellulases. EglC is most active towards xyloglucan and therefore is functionally different from the other two endoglucanases from A. niger, EglA and EglB, which exhibit the greatest activity towards β-glucan. Although the mode of action of EglC is not known, this enzyme represents a new enzyme function involved in plant cell wall polysaccharide degradation by A. niger.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

scholarly journals PUL-Mediated Plant Cell Wall Polysaccharide Utilization in the Gut Bacteroidetes

2021 ◽  
Vol 22 (6) ◽  
pp. 3077
Author(s):  
Zhenzhen Hao ◽  
Xiaolu Wang ◽  
Haomeng Yang ◽  
Tao Tu ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Cell Wall ◽  
Microbial Community ◽  
Gut Microbiota ◽  
Plant Cell ◽  
Plant Cell Wall ◽  
Cell Wall Polysaccharide ◽  
Prominent Feature ◽  
Cell Wall Polysaccharides ◽  
Gut Microbial Community

Plant cell wall polysaccharides (PCWP) are abundantly present in the food of humans and feed of livestock. Mammalians by themselves cannot degrade PCWP but rather depend on microbes resident in the gut intestine for deconstruction. The dominant Bacteroidetes in the gut microbial community are such bacteria with PCWP-degrading ability. The polysaccharide utilization systems (PUL) responsible for PCWP degradation and utilization are a prominent feature of Bacteroidetes. In recent years, there have been tremendous efforts in elucidating how PULs assist Bacteroidetes to assimilate carbon and acquire energy from PCWP. Here, we will review the PUL-mediated plant cell wall polysaccharides utilization in the gut Bacteroidetes focusing on cellulose, xylan, mannan, and pectin utilization and discuss how the mechanisms can be exploited to modulate the gut microbiota.

scholarly journals A proof of concept for machine learning-based virtual knapping using neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jordy Didier Orellana Figueroa ◽  
Jonathan Scott Reeves ◽  
Shannon P. McPherron ◽  
Claudio Tennie
Keyword(s):  
Machine Learning ◽  
Raw Materials ◽  
Training Sample ◽  
Stone Tool ◽  
Proof Of Concept ◽  
Surface Information ◽  
Archaeological Data ◽  
Testing Dataset ◽  
Intact Core ◽  
Key Variables

AbstractPrehistoric stone tools are an important source of evidence for the study of human behavioural and cognitive evolution. Archaeologists use insights from the experimental replication of lithics to understand phenomena such as the behaviours and cognitive capacities required to manufacture them. However, such experiments can require large amounts of time and raw materials, and achieving sufficient control of key variables can be difficult. A computer program able to accurately simulate stone tool production would make lithic experimentation faster, more accessible, reproducible, less biased, and may lead to reliable insights into the factors that structure the archaeological record. We present here a proof of concept for a machine learning-based virtual knapping framework capable of quickly and accurately predicting flake removals from 3D cores using a conditional adversarial neural network (CGAN). We programmatically generated a testing dataset of standardised 3D cores with flakes knapped from them. After training, the CGAN accurately predicted the length, volume, width, and shape of these flake removals using the intact core surface information alone. This demonstrates the feasibility of machine learning for investigating lithic production virtually. With a larger training sample and validation against archaeological data, virtual knapping could enable fast, cheap, and highly-reproducible virtual lithic experimentation.

scholarly journals Microbial Contamination in Some Commercial Biscuits in Baghdad City

2010 ◽  
Vol 7 (2) ◽  
pp. 867-875
Author(s):  
Baghdad Science Journal
Keyword(s):  
Staphylococcus Aureus ◽  
Aspergillus Niger ◽  
Bacillus Cereus ◽  
Microbial Contamination ◽  
Local Markets ◽  
Esherichia Coli ◽  
Bacteria And Fungi ◽  
Penicillium Spp ◽  
Number Of Bacteria

This study has been conducted to know the level of microbial ( bacteria and fungi) contamination in 5 types of biscuits from local markets of Baghdad city. Fifty samples (ten sample for each kind of biscuit) were studed,Two are local,others are Iranian,Turkish,and Holandies. The following results have been achieved :1. The highest number of bacteria was 21.6×103 cell/g in Iranian biscuit while the lowest number was 14.3× 103 cell/g in local biscuit No.1 . The highest number of fungi was 16×103 colony/g and the lowest number was 5.3×103 colony/g in the Iranian and the local biscuit No.1,respectively.2. Staphylococcus aureus was the major bacteria appeared at highest level of 100% in Turkish biscuit. The lowest percentage was found in Hollandian biscuit with 37.28%. Bacillus cereus was the major bacteria with a percentage of 100% in local biscuit No.2 where as the lowest was in local biscuit No,1with a percentage of 20.93%, while it was not existed in Turkish biscuit. Esherichia coli was found in Hollandian biscuit at highest rate of 38.98% , the lowest value was appeared in Iranian biscuit with 28.16% while it was not exited in local biscuit No.1,2 and Turkish biscuit.3. Aspergillus niger appeared at highest level of 66.66% in Hollandian biscuit, while was the lowest 37.73% in local biscuit No.1 and not existed in local biscuit No.2, The highest value of A.flavus was 69.76% in local biscuit No.2 and the lowest value in Hollandian biscuit in percentage 8.33%. It has not appeared in Iranian and Turkish biscuit. The A. terreus appeared at highest rate in Turkish biscuit with 33.33% , the lowest value was in local biscuit No.2 at 11.62% and was not appeared in Hollandian biscuit.The Penicillium spp. Was found at highest rate 25% in Hollandian biscuit , the lowest value of 9.52% was appeared in Turkish biscuit.

scholarly journals Emotion Detection using Social Media and Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4491-4494
Author(s):  
Mr. Bhavar Shivam S.
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Social Networking Sites ◽  
Training Dataset ◽  
Emotion Detection ◽  
Pos Tagging ◽  
Testing Dataset ◽  
Svm Algorithm ◽  
The Right ◽  
Negative Sentiment

Today we do a lot of things online from shopping to data sharing on social networking sites. Social networking (SNS) is good for releasing stress and depression by sharing one’s thoughts. Thus, emotion detection has become a hot trend to day. But there is a problem in analyzing emotions on a SNS like twitter as it generates lakhs of tweets each day and it is hard to keep track of the emotion behind each tweet as it is impossible for a human being to read and decide the emotions behind tweets. So, to help understand behind the texts in a SNS site we thought of designing a project which will keep track of the tweets and predict the right emotion behind the tweets whether they have a positive or a negative sentiment behind them. This thought of project can be achieved by a integration of SNS with NLP and machine learning together. For SNS we will use Twitter as it generates a lot of data which is accessible freely using an API. First, we will enter a keyword and fetch tweets from the twitter. Then stop words will be removed from these tweets using NLTK stop words database. Then the tweets will be passed for POS tagging and only right form of grammatical words will be kept and others will be removed. Then we create a training dataset with two types positive and negative. Then SVM algorithm will be trained using this training dataset. Then each tweet will be passed to the SVM as testing dataset which in turn will return classification of each tweet as a whole in two classes positive and negative. Thus, our application will be helpful in recognizing emotion behind a tweet.

scholarly journals Automatic phonetic classification of vocalic allophones in Tol

2021 ◽  
Vol 6 (1) ◽  
pp. 403
Author(s):  
Marie Bissell
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Indigenous Languages ◽  
Diverse Range ◽  
Detailed Understanding ◽  
Allophonic Variation ◽  
Phonetic Classification

The aim of the present study involving automatic phonetic classification of /e/ and /u/ tokens in Tol is two-fold: first, I test existing claims about allophonic variation within these vowel classes, and second, I investigate allophonic variation within these vowel classes that has yet to be documented. The acoustic phonetic classifications derived in the present study contribute to a more detailed understanding of the allophonic systems operating within the Tol language. Operationalizing machine learning algorithms to investigate under-resourced, indigenous languages has the potential to provide detailed insights into the acoustic phonetic dynamics of a diverse range of vocalic systems.

scholarly journals Regulation of the Feruloyl Esterase (faeA) Gene from Aspergillus niger

1999 ◽  
Vol 65 (12) ◽  
pp. 5500-5503 ◽  
Author(s):  
Ronald P. de Vries ◽  
Jaap Visser
Keyword(s):  
Cell Wall ◽  
Aspergillus Niger ◽  
Ferulic Acid ◽  
Methoxy Group ◽  
Plant Cell Wall ◽  
Ring Structure ◽  
Feruloyl Esterase ◽  
Cell Wall Polysaccharides ◽  
Aromatic Residues ◽  
Encoding Gene

ABSTRACT Feruloyl esterases can remove aromatic residues (e.g., ferulic acid) from plant cell wall polysaccharides (xylan, pectin) and are essential for complete degradation of these polysaccharides. Expression of the feruloyl esterase-encoding gene (faeA) fromAspergillus niger depends on d-xylose (expression is mediated by XlnR, the xylanolytic transcriptional activator) and on a second system that responds to aromatic compounds with a defined ring structure, such as ferulic acid and vanillic acid. Several compounds were tested, and all of the inducing compounds contained a benzene ring which had a methoxy group at C-3 and a hydroxy group at C-4 but was not substituted at C-5. Various aliphatic groups occurred at C-1. faeA expression in the presence of xylose or ferulic acid was repressed by glucose. faeA expression in the presence of ferulic acid and xylose was greater thanfaeA expression in the presence of either compound alone. The various inducing systems allow A. niger to produce feruloyl esterase not only during growth on xylan but also during growth on other ferulic acid-containing cell wall polysaccharides, such as pectin.

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